import pandas as pd
import os
import glob
import re
import matplotlib.pyplot as plt

#!pip install cufflink
#!pip install plotly

import numpy as np
import seaborn as sns

#import cufflinks as cf

import plotly.graph_objects as go
from plotly.subplots import make_subplots

from plotly.offline import init_notebook_mode, download_plotlyjs,plot,iplot
import plotly.express as px

%matplotlib inline
#cf.go_offline()

init_notebook_mode(connected=True)

importing 2015/16 data for analyzing leicester's league victory

pl16 = pd.read_csv('pl_15-16.csv')
#pl17 = pd.read_csv('pl_16-17.csv')
#pl18 = pd.read_csv('pl_17-18.csv')
#pl19 = pd.read_csv('pl_18-19.csv')
#pl20 = pd.read_csv('pl_19-20.csv')

pl16.drop("Unnamed: 0", axis=1, inplace=True)

pl16.describe().iloc[:,30:]

	Goals with right foot	Goals with left foot	Hit woodwork	Goals per match	Penalties scored	Freekicks scored	Shots	Shots on target	Big chances missed	Saves	Penalties saved	Punches	High Claims	Catches	Sweeper clearances	Throw outs	Goal Kicks
count	681.000000	681.000000	681.000000	435.000000	435.000000	435.000000	435.000000	435.000000	435.000000	76.000000	76.000000	76.000000	76.000000	76.000000	76.000000	76.000000	76.000000
mean	0.757709	0.358297	0.328928	0.072115	0.156322	0.048276	18.000000	6.137931	1.427586	28.618421	0.144737	6.000000	13.000000	3.526316	7.355263	40.144737	82.842105
std	2.020855	1.080969	0.864598	0.121436	0.650009	0.253938	25.590087	9.899230	2.902593	36.768453	0.508696	10.919096	18.285149	5.095027	11.260200	57.951406	108.159765
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	7.000000	1.000000	0.000000	7.000000	0.000000	0.000000	1.500000	0.000000	1.500000	7.000000	19.500000
75%	0.000000	0.000000	0.000000	0.110000	0.000000	0.000000	28.500000	9.000000	1.000000	60.500000	0.000000	8.250000	22.250000	6.250000	10.500000	60.500000	156.000000
max	19.000000	13.000000	7.000000	0.800000	6.000000	2.000000	158.000000	75.000000	22.000000	121.000000	3.000000	66.000000	81.000000	18.000000	46.000000	204.000000	378.000000

#pl01 = pd.read_csv('pl_2000-2001.csv')

pl_full_data = pd.read_csv("English_Premier_League_data_2009-2019.csv")

pl_data = pl_full_data.copy()

Match data statistics

pl_full_data

	Div	Date	HomeTeam	AwayTeam	FTHG	FTAG	FTR	HTHG	HTAG	HTR	...	HST	AST	HF	AF	HC	AC	HY	AY	HR	AR
0	E0	2009-08-15 00:00:00	Aston Villa	Wigan	0	2	A	0	1	A	...	5	7	15	14	4	6	2	2	0	0
1	E0	2009-08-15 00:00:00	Blackburn	Man City	0	2	A	0	1	A	...	9	5	12	9	5	4	2	1	0	0
2	E0	2009-08-15 00:00:00	Bolton	Sunderland	0	1	A	0	1	A	...	3	13	16	10	4	7	2	1	0	0
3	E0	2009-08-15 00:00:00	Chelsea	Hull	2	1	H	1	1	D	...	12	3	13	15	12	4	1	2	0	0
4	E0	2009-08-15 00:00:00	Everton	Arsenal	1	6	A	0	3	A	...	5	9	11	13	4	9	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3795	E0	2019-05-12 00:00:00	Liverpool	Wolves	2	0	H	1	0	H	...	5	2	3	11	4	1	0	2	0	0
3796	E0	2019-05-12 00:00:00	Man United	Cardiff	0	2	A	0	1	A	...	10	4	9	6	11	2	3	3	0	0
3797	E0	2019-05-12 00:00:00	Southampton	Huddersfield	1	1	D	1	0	H	...	3	3	8	6	4	3	0	1	0	0
3798	E0	2019-05-12 00:00:00	Tottenham	Everton	2	2	D	1	0	H	...	3	9	10	13	7	4	0	2	0	0
3799	E0	2019-05-12 00:00:00	Watford	West Ham	1	4	A	0	2	A	...	8	9	10	10	7	2	1	0	1	0

3800 rows × 23 columns

pl_full_data.columns

Index(['Div', 'Date', 'HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTHG',
       'HTAG', 'HTR', 'Referee', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC',
       'AC', 'HY', 'AY', 'HR', 'AR'],
      dtype='object')

#dropping first column 'Div' as value is always E0
pl_data = pl_data.drop(['Div'], axis=1)

pl_data.columns=['Date','HomeTeam','AwayTeam','FT_Home_Goal','FT_Away_Goal','FT_Result','HT_Home_Goal','HT_Away_Goal',
                        'HT_Result','Referee','H_Shots','A_Shots','H_Shots_Target','A_Shots_Target','H_Foul',
                        'A_Foul','H_Corner','A_Corner','H_Yellow','A_Yellow','H_Red','A_Red']

pl_data.head()

	Date	HomeTeam	AwayTeam	FT_Home_Goal	FT_Away_Goal	FT_Result	HT_Home_Goal	HT_Away_Goal	HT_Result	Referee	...	H_Shots_Target	A_Shots_Target	H_Foul	A_Foul	H_Corner	A_Corner	H_Yellow	A_Yellow	H_Red	A_Red
0	2009-08-15 00:00:00	Aston Villa	Wigan	0	2	A	0	1	A	M Clattenburg	...	5	7	15	14	4	6	2	2	0	0
1	2009-08-15 00:00:00	Blackburn	Man City	0	2	A	0	1	A	M Dean	...	9	5	12	9	5	4	2	1	0	0
2	2009-08-15 00:00:00	Bolton	Sunderland	0	1	A	0	1	A	A Marriner	...	3	13	16	10	4	7	2	1	0	0
3	2009-08-15 00:00:00	Chelsea	Hull	2	1	H	1	1	D	A Wiley	...	12	3	13	15	12	4	1	2	0	0
4	2009-08-15 00:00:00	Everton	Arsenal	1	6	A	0	3	A	M Halsey	...	5	9	11	13	4	9	0	0	0	0

5 rows × 22 columns

labels = ['Home Team Wins','Away Team Wins','Draw']
ft_results = pd.DataFrame(pl_data['FT_Result'].value_counts())
ft_results.head()

	FT_Result
H	1758
A	1102
D	940

values = ft_results["FT_Result"].values
values

array([1758, 1102,  940], dtype=int64)

home team- away team record

# Pie chart
colors = ['#99ff99','#ff9999','#ffcc99']
sizes = [30, 45, 10]
# only "explode" the 2nd slice (i.e. 'Hogs')
explode = (0.1, 0, 0)  
fig1, ax1 = plt.subplots()

ax1.pie(values, explode=explode, labels=labels, autopct='%2.1f%%',colors= colors,
        shadow=True, startangle=90,textprops={'fontsize': 16})
# Equal aspect ratio ensures that pie is drawn as a circle
ax1.axis('equal')  
plt.tight_layout()
plt.show()

preparing data for BAR CHART RACE and importing necessary files

import sys
!{sys.executable} -m pip install bar_chart_race

Requirement already satisfied: bar_chart_race in c:\users\nikhi\anaconda3\lib\site-packages (0.1.0)
Requirement already satisfied: matplotlib>=3.1 in c:\users\nikhi\anaconda3\lib\site-packages (from bar_chart_race) (3.4.2)
Requirement already satisfied: pandas>=0.24 in c:\users\nikhi\anaconda3\lib\site-packages (from bar_chart_race) (1.3.2)
Requirement already satisfied: numpy>=1.16 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (1.20.3)
Requirement already satisfied: cycler>=0.10 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (0.10.0)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (2.8.2)
Requirement already satisfied: pillow>=6.2.0 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (8.3.1)
Requirement already satisfied: pyparsing>=2.2.1 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (2.4.7)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\nikhi\anaconda3\lib\site-packages (from matplotlib>=3.1->bar_chart_race) (1.3.1)
Requirement already satisfied: six in c:\users\nikhi\anaconda3\lib\site-packages (from cycler>=0.10->matplotlib>=3.1->bar_chart_race) (1.16.0)
Requirement already satisfied: pytz>=2017.3 in c:\users\nikhi\anaconda3\lib\site-packages (from pandas>=0.24->bar_chart_race) (2021.1)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

import bar_chart_race as bcr
from IPython.display import HTML
import warnings
warnings.filterwarnings("ignore")
from IPython.display import Video

pgfgg = pd.read_csv('all_season/Premier League 00_01.csv')

pgfgg.head()

	Position	Club	Played	Won	Drawn	Lost	GF	GA	GD	Points	season
0	1	Manchester United	38	24	8	6	79	31	48	80	2000/2001
1	2	Arsenal	38	20	10	8	63	38	25	70	2000/2001
2	3	Liverpool	38	20	9	9	71	39	32	69	2000/2001
3	4	Leeds United	38	20	8	10	64	43	21	68	2000/2001
4	5	Ipswich Town	38	20	6	12	57	42	15	66	2000/2001

dir_loc='all_season/'

year=[]
loc=[]
for file in os.listdir(dir_loc):
    print(file)
    
    if file[-6]=='9':
        year+=[1900+int(file[-6:-4])]
        loc+=[os.path.join(dir_loc,file)]
    else:
        year+=[2000+int(file[-6:-4])]
        loc+=[os.path.join(dir_loc,file)]

Premier League 00_01.csv
Premier League 01_02.csv
Premier League 02_03.csv
Premier League 03_04.csv
Premier League 04_05.csv
Premier League 05_06.csv
Premier League 06_07.csv
Premier League 07_08.csv
Premier League 08_09.csv
Premier League 09_10.csv
Premier League 10_11.csv
Premier League 11_12.csv
Premier League 12_13.csv
Premier League 13_14.csv
Premier League 14_15.csv
Premier League 15_16.csv
Premier League 16_17.csv
Premier League 17_18.csv
Premier League 18_19.csv
Premier League 19_20.csv
Premier League 92_93.csv
Premier League 93_94.csv
Premier League 94_95.csv
Premier League 95_96.csv
Premier League 96_97.csv
Premier League 97_98.csv
Premier League 98_99.csv
Premier League 99_00.csv

df_season = pd.DataFrame(loc,year)
df_season = df_season.sort_index()
df_season.reset_index(inplace=True)
df_season.rename(columns = {0:'loc','index':'year'}, inplace = True)

df_season

	year	loc
0	1993	all_season/Premier League 92_93.csv
1	1994	all_season/Premier League 93_94.csv
2	1995	all_season/Premier League 94_95.csv
3	1996	all_season/Premier League 95_96.csv
4	1997	all_season/Premier League 96_97.csv
5	1998	all_season/Premier League 97_98.csv
6	1999	all_season/Premier League 98_99.csv
7	2000	all_season/Premier League 99_00.csv
8	2001	all_season/Premier League 00_01.csv
9	2002	all_season/Premier League 01_02.csv
10	2003	all_season/Premier League 02_03.csv
11	2004	all_season/Premier League 03_04.csv
12	2005	all_season/Premier League 04_05.csv
13	2006	all_season/Premier League 05_06.csv
14	2007	all_season/Premier League 06_07.csv
15	2008	all_season/Premier League 07_08.csv
16	2009	all_season/Premier League 08_09.csv
17	2010	all_season/Premier League 09_10.csv
18	2011	all_season/Premier League 10_11.csv
19	2012	all_season/Premier League 11_12.csv
20	2013	all_season/Premier League 12_13.csv
21	2014	all_season/Premier League 13_14.csv
22	2015	all_season/Premier League 14_15.csv
23	2016	all_season/Premier League 15_16.csv
24	2017	all_season/Premier League 16_17.csv
25	2018	all_season/Premier League 17_18.csv
26	2019	all_season/Premier League 18_19.csv
27	2020	all_season/Premier League 19_20.csv

data=pd.read_csv(df_season.loc[0,'loc'])
data.columns

Index(['Position', 'Club', 'Played', 'Won', 'Drawn', 'Lost', 'GF', 'GA', 'GD',
       'Points', 'season'],
      dtype='object')

final_data=data[['Position','Club','season','Points']]
final_data.loc[:,'year']=1993


for j in range(1,len(df_season)):
    if j==14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.rename(columns={'PointsForm':'Points',}, inplace=True)
        df_add.loc[:,'year']=(1993+j)
        conc=df_add[['Position','Club','season','Points','year']]
        final_data=pd.concat([final_data,conc])        
    
    elif j!=14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.loc[:,'year']=(1993+j)
        conc=df_add[['Position','Club','season','Points','year']]
        final_data=pd.concat([final_data,conc])

final_data

	Position	Club	season	Points	year
0	1	Manchester United	1992/1993	84.0	1993
1	2	Aston Villa	1992/1993	74.0	1993
2	3	Norwich City	1992/1993	72.0	1993
3	4	Blackburn Rovers	1992/1993	71.0	1993
4	5	Queens Park Rangers	1992/1993	63.0	1993
...	...	...	...	...	...
15	16	West Ham United	2019/2020	27.0	2020
16	17	Watford	2019/2020	27.0	2020
17	18	Bournemouth	2019/2020	27.0	2020
18	19	Aston Villa	2019/2020	25.0	2020
19	20	Norwich City	2019/2020	21.0	2020

566 rows × 5 columns

final_data.describe()

	Position	Points	year
count	566.000000	566.000000	566.000000
mean	10.616608	52.210247	2006.367491
std	5.850619	16.272567	8.143792
min	1.000000	11.000000	1993.000000
25%	6.000000	41.000000	1999.000000
50%	11.000000	49.000000	2006.000000
75%	16.000000	62.750000	2013.000000
max	22.000000	100.000000	2020.000000

final_stats_data=data[["Club","Played","Won","Drawn","Lost","GF","GA","GD","Points","season"]]


final_stats_data.loc[:,'year']=1993


for j in range(1,len(df_season)):
    if j==14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.rename(columns={'PointsForm':'Points',}, inplace=True)
        df_add.rename(columns={'\tPlayed':'Played'}, inplace=True)
        df_add.loc[:,'year']=(1993+j)
        conc=df_add[["Club","Played","Won","Drawn","Lost","GF","GA","GD","Points","season","year"]]
        final_stats_data=pd.concat([final_stats_data,conc])        
    
    elif j!=14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.loc[:,'year']=(1993+j)
        df_add.rename(columns={'\tPlayed':'Played'}, inplace=True)
        conc=df_add[["Club","Played","Won","Drawn","Lost","GF","GA","GD","Points","season","year"]]
        final_stats_data=pd.concat([final_stats_data,conc])

final_stats_data

	Club	Played	Won	Drawn	Lost	GF	GA	GD	Points	season	year
0	Manchester United	42	24	12	6	67	31	36	84.0	1992/1993	1993
1	Aston Villa	42	21	11	10	57	40	17	74.0	1992/1993	1993
2	Norwich City	42	21	9	12	61	65	-4	72.0	1992/1993	1993
3	Blackburn Rovers	42	20	11	11	68	46	22	71.0	1992/1993	1993
4	Queens Park Rangers	42	17	12	13	63	55	8	63.0	1992/1993	1993
...	...	...	...	...	...	...	...	...	...	...	...
15	West Ham United	29	7	6	16	35	50	-15	27.0	2019/2020	2020
16	Watford	29	6	9	14	27	44	-17	27.0	2019/2020	2020
17	Bournemouth	29	7	6	16	29	47	-18	27.0	2019/2020	2020
18	Aston Villa	28	7	4	17	34	56	-22	25.0	2019/2020	2020
19	Norwich City	29	5	6	18	25	52	-27	21.0	2019/2020	2020

566 rows × 11 columns

file_name = 'prem_scoreboard.xlsx'
  
# saving the excel
final_stats_data.to_excel(file_name)

data for bar chart point race

final1 = final_data.copy()
final2 = final1.set_index(['season','Club'])
final3 = final1.set_index(['season','Club']).unstack()['Points']
final3.fillna(0,inplace = True)

final3

Club	Arsenal	Aston Villa	Barnsley	Birmingham City	Blackburn Rovers	Blackpool	Bolton Wanderers	Bournemouth	Bradford City	Brighton and Hove Albion	...	Swansea City	Swindon Town	Tottenham Hotspur	Watford	West Bromwich Albion	West Ham United	WestHam United	Wigan Athletic	Wimbledon	Wolverhampton Wanderers
season
1992/1993	56.0	74.0	0.0	0.0	71.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	59.0	0.0	0.0	0.0	0.0	0.0	54.0	0.0
1993/1994	71.0	57.0	0.0	0.0	84.0	0.0	0.0	0.0	0.0	0.0	...	0.0	30.0	45.0	0.0	0.0	52.0	0.0	0.0	65.0	0.0
1994/1995	51.0	48.0	0.0	0.0	89.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	62.0	0.0	0.0	50.0	0.0	0.0	56.0	0.0
1995/1996	63.0	63.0	0.0	0.0	61.0	0.0	29.0	0.0	0.0	0.0	...	0.0	0.0	61.0	0.0	0.0	51.0	0.0	0.0	41.0	0.0
1996/1997	68.0	61.0	0.0	0.0	42.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	46.0	0.0	0.0	42.0	0.0	0.0	56.0	0.0
1997/1998	78.0	57.0	35.0	0.0	58.0	0.0	40.0	0.0	0.0	0.0	...	0.0	0.0	44.0	0.0	0.0	56.0	0.0	0.0	44.0	0.0
1998/1999	78.0	55.0	0.0	0.0	35.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	47.0	0.0	0.0	57.0	0.0	0.0	42.0	0.0
1999/2000	73.0	58.0	0.0	0.0	0.0	0.0	0.0	0.0	36.0	0.0	...	0.0	0.0	53.0	24.0	0.0	55.0	0.0	0.0	33.0	0.0
2000/2001	70.0	54.0	0.0	0.0	0.0	0.0	0.0	0.0	26.0	0.0	...	0.0	0.0	49.0	0.0	0.0	0.0	42.0	0.0	0.0	0.0
2001/2002	87.0	50.0	0.0	0.0	46.0	0.0	40.0	0.0	0.0	0.0	...	0.0	0.0	50.0	0.0	0.0	53.0	0.0	0.0	0.0	0.0
2002/2003	78.0	45.0	0.0	48.0	60.0	0.0	44.0	0.0	0.0	0.0	...	0.0	0.0	50.0	0.0	26.0	42.0	0.0	0.0	0.0	0.0
2003/2004	90.0	56.0	0.0	50.0	44.0	0.0	53.0	0.0	0.0	0.0	...	0.0	0.0	45.0	0.0	0.0	0.0	0.0	0.0	0.0	33.0
2004/2005	83.0	47.0	0.0	45.0	42.0	0.0	58.0	0.0	0.0	0.0	...	0.0	0.0	52.0	0.0	34.0	0.0	0.0	0.0	0.0	0.0
2005/2006	67.0	42.0	0.0	34.0	63.0	0.0	56.0	0.0	0.0	0.0	...	0.0	0.0	65.0	0.0	30.0	55.0	0.0	51.0	0.0	0.0
2006/2007	68.0	50.0	0.0	0.0	52.0	0.0	56.0	0.0	0.0	0.0	...	0.0	0.0	60.0	28.0	0.0	41.0	0.0	38.0	0.0	0.0
2007/2008	83.0	60.0	0.0	35.0	58.0	0.0	37.0	0.0	0.0	0.0	...	0.0	0.0	46.0	0.0	0.0	49.0	0.0	40.0	0.0	0.0
2008/2009	72.0	62.0	0.0	0.0	41.0	0.0	41.0	0.0	0.0	0.0	...	0.0	0.0	51.0	0.0	32.0	51.0	0.0	45.0	0.0	0.0
2009/2010	75.0	64.0	0.0	50.0	50.0	0.0	39.0	0.0	0.0	0.0	...	0.0	0.0	70.0	0.0	0.0	35.0	0.0	36.0	0.0	38.0
2010/2011	68.0	48.0	0.0	39.0	43.0	39.0	46.0	0.0	0.0	0.0	...	0.0	0.0	62.0	0.0	47.0	33.0	0.0	42.0	0.0	40.0
2011/2012	70.0	38.0	0.0	0.0	31.0	0.0	36.0	0.0	0.0	0.0	...	47.0	0.0	69.0	0.0	47.0	0.0	0.0	43.0	0.0	25.0
2012/2013	73.0	41.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	46.0	0.0	72.0	0.0	49.0	46.0	0.0	36.0	0.0	0.0
2013/2014	79.0	38.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	42.0	0.0	69.0	0.0	36.0	40.0	0.0	0.0	0.0	0.0
2014/2015	75.0	38.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	56.0	0.0	64.0	0.0	44.0	47.0	0.0	0.0	0.0	0.0
2015/2016	71.0	17.0	0.0	0.0	0.0	0.0	0.0	42.0	0.0	0.0	...	47.0	0.0	70.0	45.0	43.0	62.0	0.0	0.0	0.0	0.0
2016/2017	75.0	0.0	0.0	0.0	0.0	0.0	0.0	46.0	0.0	0.0	...	41.0	0.0	86.0	40.0	45.0	45.0	0.0	0.0	0.0	0.0
2017/2018	63.0	0.0	0.0	0.0	0.0	0.0	0.0	44.0	0.0	40.0	...	33.0	0.0	77.0	41.0	31.0	42.0	0.0	0.0	0.0	0.0
2018/2019	70.0	0.0	0.0	0.0	0.0	0.0	0.0	45.0	0.0	36.0	...	0.0	0.0	71.0	50.0	0.0	52.0	0.0	0.0	0.0	57.0
2019/2020	40.0	25.0	0.0	0.0	0.0	0.0	0.0	27.0	0.0	29.0	...	0.0	0.0	41.0	27.0	0.0	27.0	0.0	0.0	0.0	43.0

28 rows × 50 columns

final4 = final3
bcr.bar_chart_race(
    df=final4,
    filename='PL_BarChartRace.mp4',
    orientation='h',
    sort='desc',
    n_bars=20,
    fixed_order=False,
    fixed_max=False,
    steps_per_period=50,
    interpolate_period=False,
    label_bars=True,
    bar_size=.95,
    period_label={'x': .99, 'y': .25, 'ha': 'right', 'va': 'center'},
    #period_fmt='%B %d, %Y',
    period_summary_func=lambda v, r: {'x': .99, 'y': .18,'s': '',
                                      'ha': 'right', 'size': 8, 'family': 'Courier New'},
    #perpendicular_bar_func='median',
    period_length=1500,
    figsize=(8,4),
    dpi=500,
    cmap='dark24',
    title='Premier League Standings 1992-2020',
    title_size=15,
    bar_label_size=7,
    tick_label_size=5,
    shared_fontdict={'color' : '.1'},
    scale='linear',
    writer=None,
    fig=None,
    bar_kwargs={'alpha': .7},
    filter_column_colors=True)

Video("PL_BarChartRace.mp4", width=1000, height=800, embed=True)

Leciester City's road to Glory

Season - 2015/16

pl16.head(2)

	Name	Position	Appearances	Clean sheets	Goals conceded	Tackles	Tackle success %	Last man tackles	Blocked shots	Interceptions	...	Shooting accuracy %	Big chances missed	Saves	Penalties saved	Punches	High Claims	Catches	Sweeper clearances	Throw outs	Goal Kicks
0	Rolando Aarons	Midfielder	10	NaN	NaN	13.0	77%	NaN	0.0	6.0	...	50%	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Almen Abdi	Midfielder	32	NaN	NaN	83.0	78%	NaN	10.0	32.0	...	26%	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

2 rows × 53 columns

Top Goalscorers in 2015-16

pl16 = pl16[pl16['Appearances']!=0]
goalscorers = pl16.nlargest(5, ['Goals']).reset_index()

from matplotlib.patches import Patch
custom_lines = [ Patch(facecolor='Blue', edgecolor='black',label='Leicester Goalscorers'),
                Patch(facecolor='lightgrey', edgecolor='white',label='Others')]




color = ["blue" if i==2 or i==4 else "lightgrey" for i in goalscorers.index]
edgecolor= ["black" if i==2 or i==4 else "white" for i in goalscorers.index]
#goalscorers.iplot(kind='bar',x='Name',y='Goals',title='Top Goalscorers - 15/16', xTitle='Players', yTitle='Goals',color='red')
ax = goalscorers.plot.bar(x="Name",y = "Goals", color= color,figsize =(10,8),edgecolor= edgecolor,legend = None)
plt.xticks(rotation = 0,fontsize = 13)
plt.yticks(fontsize = 13)
plt.xlabel("Players",fontsize = 18)
plt.ylabel("Goals",fontsize = 18)
plt.title("Top 5 Goalscorers - 15/16",fontsize = 20,loc='left')
ax.yaxis.grid(zorder = 0)
ax.set_axisbelow(True)

for x,y,z in zip(goalscorers.index, goalscorers["Goals"], goalscorers["Goals"]):
    plt.text(x, y, z,verticalalignment='bottom',horizontalalignment='center',fontsize = 15,weight='bold')

ax.legend(custom_lines, ['Leicester Goalscorers', 'Others'],fontsize = 15)

plt.show()

top 5 Playmakers

playmakers = pl16.nlargest(5, ['Assists']).reset_index()

color = ["purple" if i==2 or i==4 else "lightgrey" for i in playmakers.index]
edgecolor= ["black" if i==2 or i==4 else "white" for i in playmakers.index]

from matplotlib.patches import Patch
custom_lines = [ Patch(facecolor='purple', edgecolor='black',label='Leicester Goalscorers'),
                Patch(facecolor='lightgrey', edgecolor='white',label='Others')]


#goalscorers.iplot(kind='bar',x='Name',y='Goals',title='Top Goalscorers - 15/16', xTitle='Players', yTitle='Goals',color='red')
ax = playmakers.plot.bar(x="Name",y = "Assists", color= color,figsize =(10,8),edgecolor= edgecolor,legend = None)
plt.xticks(rotation = 0,fontsize = 13)
plt.yticks(fontsize = 13)
plt.xlabel("Players",fontsize = 18)
plt.ylabel("Assists",fontsize = 18)
plt.title("Top 5 Playmakers - 15/16",fontsize = 20,loc='left')
ax.yaxis.grid(zorder = 0)
ax.set_axisbelow(True)


ax.legend(custom_lines, ['Leicester Playmakers', 'Others'],fontsize = 15)



for x,y,z in zip(playmakers.index, playmakers["Assists"], playmakers["Assists"]):
    plt.text(x, y, z,verticalalignment='bottom',horizontalalignment='center',fontsize = 15,weight='bold')

plt.show()

#Creating a new column 'Involvements'

pl16['Involvements'] = pl16['Goals'] + pl16['Assists']
pl16[['Name','Involvements']].head()

	Name	Involvements
0	Rolando Aarons	2
1	Almen Abdi	2
2	Abdul Rahman Baba	1
3	Mehdi Abeid	0
4	Tammy Abraham	0

Most Involvements

from matplotlib.lines import Line2D

involvements = pl16.nlargest(5, 'Involvements')
inv = involvements.reset_index().drop('index',axis=1)

ax = inv.plot(x='Name',y='Involvements',figsize=(10,8),color ='blue',alpha= 0.5)

plt.xticks(fontsize = 15)
plt.yticks(fontsize = 13)
plt.xlabel("Players",fontsize = 18)
plt.ylabel("Involvments",fontsize = 18)
plt.title("Top 5 Players with Most Involvments(Goals + Assists) - 15/16",fontsize = 25)

ax=plt.gca()
ax.yaxis.grid(zorder = 0)
ax.set_axisbelow(True)



legend_elements = [Line2D([0], [0], marker='o', color='w', label='Scatter',markerfacecolor='g', markersize=15)]
ax.legend(legend_elements, ['Leicester Players'],fontsize = 15)

for x,y,z in zip(inv.index, inv["Involvements"], inv["Involvements"]):
    plt.text(x+0.2, y, z,verticalalignment='bottom',horizontalalignment='center',fontsize = 15,weight='bold')
    if x == 0 or x == 1:
        plt.scatter(x,y,c="green",s= 150)

goals conceded by defenders per game

#Making a dataframe with only defenders
defenders = pl16[pl16['Position']=='Defender']
plt.figure(figsize = (12,8))
#Selecting defenders with more than 30 appearances and making a new column 'Conceded per game',
#which is Goals conceded/Appearances.


from matplotlib.patches import Patch
custom_lines = [ Patch(facecolor='red', edgecolor='black'),
                Patch(facecolor='lightgrey', edgecolor='white')]

top_def_stats = defenders[defenders['Appearances']>30]

top_def_stats['Conceded per game'] = top_def_stats['Goals conceded']/top_def_stats['Appearances']
least_conceded = top_def_stats.nsmallest(5, 'Conceded per game')
a_data = least_conceded.reset_index().drop("index",axis=1)
color = ["red","lightgrey","lightgrey","lightgrey","red"]
edgecolor= ["black" if i==0 or i==4 else "white" for i in a_data.index]
ax1 = plt.bar(a_data["Name"],a_data["Conceded per game"],color = color,edgecolor=edgecolor)

plt.xticks(fontsize = 15)
plt.yticks(np.arange(0,1,0.05),fontsize = 13)


plt.xlabel("Defenders",fontsize = 18)
plt.ylabel("Goals Conceded Per Game",fontsize = 18)
plt.title("Top 5 Defenders with least Goals conceded per Game - 15/16",fontsize = 25)



ax=plt.gca()
ax.yaxis.grid(zorder = 0)
ax.set_axisbelow(True)

ax.legend(custom_lines, ['Leicester Defenders', 'Others'],fontsize = 15,loc='best')

for x,y,z in zip(a_data.index, a_data["Conceded per game"], a_data["Conceded per game"]):
    plt.text(x, y, round(z,3),verticalalignment='bottom',horizontalalignment='center',fontsize = 15,weight='bold')

plt.show()

top 5 goalkeepers in the 2015/16 season

Leicester keeper highlighted in yellow (Kasper Schmeichel)

gks.style.apply(lambda x:['background:yellow'
                             if (x.name=="Kasper Schmeichel ")
                             else '' for i in x], axis=1)

	Appearances	Clean sheets	Goals conceded	Saves
Name
Lukasz Fabianski	37	9.000000	51.000000	115.000000
Heurelho Gomes	38	11.000000	49.000000	121.000000
Hugo Lloris	37	13.000000	34.000000	88.000000
Kasper Schmeichel	38	15.000000	36.000000	99.000000

Regression model to predict the position of teams in the premier league- based on goal differential

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd

reg_data=data[["Club","Position","Played","Won","Drawn","Lost","GF","GA","GD","Points","season"]]

reg_data.loc[:,'year']=1993


for j in range(1,len(df_season)):
    if j==14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.rename(columns={'PointsForm':'Points',}, inplace=True)
        df_add.rename(columns={'\tPlayed':'Played'}, inplace=True)
        df_add.loc[:,'year']=(1993+j)
        conc=df_add[["Club","Position","Played","Won","Drawn","Lost","GF","GA","GD","Points","season","year"]]
        reg_data=pd.concat([reg_data,conc])        
    
    elif j!=14:
        df_add =pd.read_csv(df_season.loc[j,'loc'])
        df_add.loc[:,'year']=(1993+j)
        df_add.rename(columns={'\tPlayed':'Played'}, inplace=True)
        conc=df_add[["Club","Position","Played","Won","Drawn","Lost","GF","GA","GD","Points","season","year"]]
        reg_data=pd.concat([reg_data,conc])

reg_data

	Club	Position	Played	Won	Drawn	Lost	GF	GA	GD	Points	season	year
0	Manchester United	1	42	24	12	6	67	31	36	84.0	1992/1993	1993
1	Aston Villa	2	42	21	11	10	57	40	17	74.0	1992/1993	1993
2	Norwich City	3	42	21	9	12	61	65	-4	72.0	1992/1993	1993
3	Blackburn Rovers	4	42	20	11	11	68	46	22	71.0	1992/1993	1993
4	Queens Park Rangers	5	42	17	12	13	63	55	8	63.0	1992/1993	1993
...	...	...	...	...	...	...	...	...	...	...	...	...
15	West Ham United	16	29	7	6	16	35	50	-15	27.0	2019/2020	2020
16	Watford	17	29	6	9	14	27	44	-17	27.0	2019/2020	2020
17	Bournemouth	18	29	7	6	16	29	47	-18	27.0	2019/2020	2020
18	Aston Villa	19	28	7	4	17	34	56	-22	25.0	2019/2020	2020
19	Norwich City	20	29	5	6	18	25	52	-27	21.0	2019/2020	2020

566 rows × 12 columns

reg_data[reg_data['year']==2018]

	Club	Position	Played	Won	Drawn	Lost	GF	GA	GD	Points	season	year
0	Manchester City	1	38	32	4	2	106	27	79	100.0	2017/2018	2018
1	Manchester United	2	38	25	6	7	68	28	40	81.0	2017/2018	2018
2	Tottenham Hotspur	3	38	23	8	7	74	36	38	77.0	2017/2018	2018
3	Liverpool	4	38	21	12	5	84	38	46	75.0	2017/2018	2018
4	Chelsea	5	38	21	7	10	62	38	24	70.0	2017/2018	2018
5	Arsenal	6	38	19	6	13	74	51	23	63.0	2017/2018	2018
6	Burnley	7	38	14	12	12	36	39	-3	54.0	2017/2018	2018
7	Everton	8	38	13	10	15	44	58	-14	49.0	2017/2018	2018
8	Leicester City	9	38	12	11	15	56	60	-4	47.0	2017/2018	2018
9	Newcastle United	10	38	12	8	18	39	47	-8	44.0	2017/2018	2018
10	Crystal Palace	11	38	11	11	16	45	55	-10	44.0	2017/2018	2018
11	Bournemouth	12	38	11	11	16	45	61	-16	44.0	2017/2018	2018
12	West Ham United	13	38	10	12	16	48	68	-20	42.0	2017/2018	2018
13	Watford	14	38	11	8	19	44	64	-20	41.0	2017/2018	2018
14	Brighton and Hove Albion	15	38	9	13	16	34	54	-20	40.0	2017/2018	2018
15	Huddersfield Town	16	38	9	10	19	28	58	-30	37.0	2017/2018	2018
16	Southampton	17	38	7	15	16	37	56	-19	36.0	2017/2018	2018
17	Swansea City	18	38	8	9	21	28	56	-28	33.0	2017/2018	2018
18	Stoke City	19	38	7	12	19	35	68	-33	33.0	2017/2018	2018
19	West Bromwich Albion	20	38	6	13	19	31	56	-25	31.0	2017/2018	2018

top team has the highest GD while bottom team has the lowest

this may not be true in all cases as the Goal differences may minutely vary

data_2008 = reg_data[reg_data['year']==2008]
data_2008_copy =  data_2008 

data_2008.style.apply(lambda x:['background:yellowgreen'
                             if (x["Position"]==1)
                             else 'background:red'
                             if (x["Position"]==20) 
                             else '' for i in x], axis=1)

	Club	Position	Played	Won	Drawn	Lost	GF	GA	GD	Points	season	year
0	Manchester United	1	38	27	6	5	80	22	58	87.000000	2007/2008	2008
1	Chelsea	2	38	25	10	3	65	26	39	85.000000	2007/2008	2008
2	Arsenal	3	38	24	11	3	74	31	43	83.000000	2007/2008	2008
3	Liverpool	4	38	21	13	4	67	28	39	76.000000	2007/2008	2008
4	Everton	5	38	19	8	11	55	33	22	65.000000	2007/2008	2008
5	Aston Villa	6	38	16	12	10	71	51	20	60.000000	2007/2008	2008
6	Blackburn Rovers	7	38	15	13	10	50	48	2	58.000000	2007/2008	2008
7	Portsmouth	8	38	16	9	13	48	40	8	57.000000	2007/2008	2008
8	Manchester City	9	38	15	10	13	45	53	-8	55.000000	2007/2008	2008
9	West Ham United	10	38	13	10	15	42	50	-8	49.000000	2007/2008	2008
10	Tottenham Hotspur	11	38	11	13	14	66	61	5	46.000000	2007/2008	2008
11	Newcastle United	12	38	11	10	17	45	65	-20	43.000000	2007/2008	2008
12	Middlesbrough	13	38	10	12	16	43	53	-10	42.000000	2007/2008	2008
13	Wigan Athletic	14	38	10	10	18	34	51	-17	40.000000	2007/2008	2008
14	Sunderland	15	38	11	6	21	36	59	-23	39.000000	2007/2008	2008
15	Bolton Wanderers	16	38	9	10	19	36	54	-18	37.000000	2007/2008	2008
16	Fulham	17	38	8	12	18	38	60	-22	36.000000	2007/2008	2008
17	Reading	18	38	10	6	22	41	66	-25	36.000000	2007/2008	2008
18	Birmingham City	19	38	8	11	19	46	62	-16	35.000000	2007/2008	2008
19	Derby County	20	38	1	8	29	20	89	-69	11.000000	2007/2008	2008

pd.DataFrame(data_2008.iloc[0,:])

	0
Club	Manchester United
Position	1
Played	38
Won	27
Drawn	6
Lost	5
GF	80
GA	22
GD	58
Points	87.0
season	2007/2008
year	2008
0	background-color: green

x = reg_data["GD"].values.reshape(-1,1)
y = reg_data["Position"].values

model= LinearRegression()
model.fit(x,y)
evaluation_score = model.score(x,y)
intercept = model.intercept_
slope = model.coef_

print("score",evaluation_score)

score 0.7644324848047668

y_pred = intercept + slope*x

fig,ax = plt.subplots(figsize=(10,10))
plt.scatter(x,y,label="actual data")
plt.ylim(-1,24)
plt.xlim(-90,100)
plt.gca().invert_yaxis()
ax.grid(alpha=0.4)
plt.plot(x,y_pred,c="g",label = 'regression line')
plt.xlabel("Goal Difference",fontsize = 20)
plt.ylabel("Position",fontsize = 20)
plt.xticks(fontsize = 15)
plt.yticks(np.arange(1,23,1),fontsize = 15)
ax.annotate(xy=(82,2),text='Manchester City,2017/18\n (1st position)',fontsize = 15,fontweight="bold")
ax.annotate(xy=(-89,21.5),text='Derby \nCounty\n2007/08',fontweight="bold",fontsize = 14)
#plt.title("Evaluating the Relationship between Goal Differential and the League Position in the Points Table \nPremier league 1992/1993 - 2019/2020")
plt.suptitle("Evaluating the Relationship between \nGoal Differential and the League Position \n",ha='center',fontsize=22,fontweight="bold")
plt.title('Premier League Season 1992/1993 - 2019/2020',fontsize=18)
plt.legend(fontsize=14)
plt.show()

df_2021 = pd.DataFrame(columns= ["Club","Goal Diff","Real-time position"])

getting real time data from the ongoing premier league season -2021/22

x_final = np.array([13,55,8,38,50,11,10,5,-9,-4,1,-17,-10,1,-18,-14,-26,-45,-33])
y_final = np.array([4,2,6,3,1,5,7,8,11,10,9,14,13,12,17,15,18,20,16])


df_2021["Club"] = ["Arsenal","Liverpool","Manchester United","Chelsea","Manchester City","Tottenham Hotspur","West Ham",
                   "Wolves","Southampton","Leicester City","Aston Villa","Newcastle","Brighton","Crystal Palace",
                  "Everton","Brentford","Watford","Norwich City","Leeds United"]
df_2021["Goal Diff"] = x_final
df_2021["Real-time position"] = y_final

#["Manchester City","Liverpool","Chelsea","Arsenal","Tottenham Hotspur","Manchester United]
#[50,55,38,13,11,8]
#[1,2,3,4,5,6]
 
#["Arsenal","Liverpool","Manchester United","Chelsea","Manchester City","Tottenham Hotspur"]
#[13,55,8,38,50,11]
#[4,2,6,3,1,5]

df_2021

	Club	Goal Diff	Real-time position
0	Arsenal	13	4
1	Liverpool	55	2
2	Manchester United	8	6
3	Chelsea	38	3
4	Manchester City	50	1
5	Tottenham Hotspur	11	5
6	West Ham	10	7
7	Wolves	5	8
8	Southampton	-9	11
9	Leicester City	-4	10
10	Aston Villa	1	9
11	Newcastle	-17	14
12	Brighton	-10	13
13	Crystal Palace	1	12
14	Everton	-18	17
15	Brentford	-14	15
16	Watford	-26	18
17	Norwich City	-45	20
18	Leeds United	-33	16

x_final = x_final.reshape(-1,1)
print("Goal_diff shape",x_final.shape)

Goal_diff shape (19, 1)

y_pred_final = model.predict(x_final)
y_pred_final

array([ 8.00947836, -0.94151534,  9.07507285,  2.68150592,  0.12407915,
        8.43571616,  8.64883505,  9.71442954, 12.69809411, 11.63249962,
       10.56690513, 14.40304529, 12.91121301, 10.56690513, 14.61616419,
       13.7636886 , 16.32111537, 20.37037443, 17.81294765])

print("real time results",y_final)

print("predicted league standings based on goal diff",y_pred_final)

real time results [ 4  2  6  3  1  5  7  8 11 10  9 14 13 12 17 15 18 20 16]
predicted league standings based on goal diff [ 8.00947836 -0.94151534  9.07507285  2.68150592  0.12407915  8.43571616
  8.64883505  9.71442954 12.69809411 11.63249962 10.56690513 14.40304529
 12.91121301 10.56690513 14.61616419 13.7636886  16.32111537 20.37037443
 17.81294765]

df_2021["predicted position"] = y_pred_final

df_2021.head()

	Club	Goal Diff	Real-time position	predicted position
0	Arsenal	13	4	8.009478
1	Liverpool	55	2	-0.941515
2	Manchester United	8	6	9.075073
3	Chelsea	38	3	2.681506
4	Manchester City	50	1	0.124079

df_plot2 = df_2021.sort_values("Real-time position")
df_plot2.set_index("Club",inplace=True)

fig, ax = plt.subplots(figsize = (12,10))
df_plot2.plot.scatter('Goal Diff',"Real-time position",color = "red",ax=ax,label = "Real-time position",s=60)

df_plot2.plot("Goal Diff","predicted position",color= "green",label = "Predicted",ax=ax)
df_plot2.plot.scatter("Goal Diff","predicted position",color= "green",label = "Predicted",ax=ax,s=70)
for k, v in df_plot2[["Goal Diff","Real-time position"]].iterrows():
    ax.annotate(k, v,fontsize=14)

plt.xlabel("Goal Difference",fontsize = 20)
plt.ylabel("Position",fontsize = 20)
plt.xticks(fontsize = 15)
plt.yticks(np.arange(0,23,1),fontsize = 15)
plt.grid(alpha=0.6)
plt.legend(fontsize = 15)
plt.suptitle("Position-Goal Diff Graph- Using Linear Regresssion",fontsize = 25,fontweight='bold')
plt.title("using 2021 Premier League ongoing match data",fontsize=20)
plt.gca().invert_yaxis()
plt.show()

trying to get more accurate values using Polynomial Regression

from sklearn.preprocessing import PolynomialFeatures
x = reg_data["GD"].values.reshape(-1,1)
y = reg_data["Position"].values
poly_reg = PolynomialFeatures(degree=5)
X_poly = poly_reg.fit_transform(x)
pol_reg = LinearRegression()
pol_reg.fit(X_poly, y)

LinearRegression()

x_final.shape

(19, 1)

# Predicting a new result with Polymonial Regression
poly_pred = pol_reg.predict(poly_reg.fit_transform(x_final))
#output should be 132148.43750003

poly_pred

array([ 6.71401724,  2.04130727,  7.90964258,  2.88033697,  2.21013981,
        7.17737508,  7.41665674,  8.68255368, 12.65662408, 11.18985023,
        9.76724592, 15.00394228, 12.95217198,  9.76724592, 15.29068115,
       14.13149776, 17.46068925, 20.83656775, 19.07596029])

df_2021["predicted position"] = poly_pred

df_2021

	Club	Goal Diff	Real-time position	predicted position
0	Arsenal	13	4	6.714017
1	Liverpool	55	2	2.041307
2	Manchester United	8	6	7.909643
3	Chelsea	38	3	2.880337
4	Manchester City	50	1	2.210140
5	Tottenham Hotspur	11	5	7.177375
6	West Ham	10	7	7.416657
7	Wolves	5	8	8.682554
8	Southampton	-9	11	12.656624
9	Leicester City	-4	10	11.189850
10	Aston Villa	1	9	9.767246
11	Newcastle	-17	14	15.003942
12	Brighton	-10	13	12.952172
13	Crystal Palace	1	12	9.767246
14	Everton	-18	17	15.290681
15	Brentford	-14	15	14.131498
16	Watford	-26	18	17.460689
17	Norwich City	-45	20	20.836568
18	Leeds United	-33	16	19.075960

df_plot1 = df_2021.sort_values("Real-time position")

df_plot1.set_index("Club",inplace = True)

fig, ax = plt.subplots(figsize = (12,10))
df_plot1.plot.scatter('Goal Diff',"Real-time position",color = "red",ax=ax,label = "Real-time position",s=100)

df_plot1.plot("Goal Diff","predicted position",color= "green",label = "Predicted",ax=ax)
df_plot1.plot.scatter("Goal Diff","predicted position",color= "green",label = "Predicted",ax=ax,s=60)
for k, v in df_plot1[["Goal Diff","Real-time position"]].iterrows():
    ax.annotate(k, v,fontsize = 14)

plt.xlabel("Goal Difference",fontsize = 20)
plt.ylabel("Position",fontsize = 20)
plt.xticks(fontsize = 15)
plt.yticks(np.arange(0,23,1),fontsize = 15)
plt.grid(alpha=0.6)


plt.legend(fontsize = 15)

plt.suptitle("Position-Goal Diff Graph- Using Polynomial Regresssion",fontsize = 25,fontweight='bold')
plt.title("using 2021 Premier League ongoing match data",fontsize=20)

plt.gca().invert_yaxis()
plt.show()

file_name = 'poly_reg_data.csv'
  
# saving the excel
df_plot1.to_csv(file_name)

file_name = 'linear_reg_data.csv'
  
# saving the excel
df_plot2.to_csv(file_name)